Abstract PO-041: The impact of the COVID-19 pandemic on cancer patients
Bibliographic record
Abstract
Abstract As overwhelmed health care systems are dealing with the COVID-19 pandemic, changes to oncology care have been implemented to minimize patients’ exposure to the virus. We aim to understand the impact of COVID-19 on cancer patients through a questionnaire completed by cancer patients or their caregivers. From 3/24/2020 to 4/15/2020, a total of 112 patients/caregivers completed the questionnaire. Of the 112, 81 (72%) of those who completed the survey were the patients themselves, 14 (13%) were caregivers, and for 17 (15%) it was unknown. The majority of patients (48%) were between the ages of 50 and 69, 13% were 70-79, and for 22% of the patients, their age was unknown. 66 (59%) were females, 30 (27%) were male, and for 14% the sex was unknown. Thirteen types of cancers were reported; the most common cancer were breast, lung, and colorectal. Most of the participants were from the US (70%) with 12 countries represented, including Italy (7%), Canada (4%), Australia (3%), and the UK (3%). Of the 112 patients and caregivers who completed the survey, 78 (70%) reported that they or the patients they care for were currently receiving cancer treatment. Those not currently receiving cancer treatment reported the last time they received treatment as far back as March 2008 to March 2020. Canceled or postponed appointments due to COVID-19 were reported by 32 (29%) participants. Thirteen (12%) reported treatment delay because of COVID-19. Six patients (5%) were newly diagnosed and had to make a treatment decision about a new cancer diagnosis during the COVID-19 pandemic. Twenty-one (19%) patients had to make a decision about a treatment change. Eighty-three reported on whether COVID-19 affected any treatment decisions they had to make. Of these 83, 24 (29%) reported that COVID-19 affected their treatment decision, and 23 gave an explanation. The most common explanations of how COVID-19 affected treatment decisions were “changes to travel for treatment/change in place of treatment,” “changes in travel/living situations/other personal changes,” “changes to surveillance,” “changes, delays, or not receiving treatment to decrease risk of COVID-19 infection,” “continued on treatment that is not working,” and “did not continue to pursue a clinical trial.” Symptoms of COVID-19 (coughing, fever, shortness of breath) were reported by 16 (14%) patients and caregivers. Six (5%) patients had COVID-19 testing, with one patient still awaiting results, and all of the other five tested negative. Increased anxiety about cancer treatment due to COVID-19 was reported by 72 (64%) participants. Personalized support through follow-ups was implemented in an attempt to help patients relieve some of their anxiety about their cancer treatment. Overall, changes to appointments, treatment delays, and the impact of COVID-19 on treatment decisions were reported by patients and caregivers. A general sense of uncertainty about appointments and treatment plans was reported. Citation Format: Lola Rahib, Zach Kaufman, Erika Vial Monteverdi. The impact of the COVID-19 pandemic on cancer patients [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-041.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".